Predicting tissue-specific expressions based on sequence characteristics.

Abstract:

:In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.

journal_name

BMB Rep

journal_title

BMB reports

authors

Paik H,Ryu T,Heo HS,Seo SW,Lee D,Hur CG

doi

10.5483/BMBRep.2011.44.4.250

subject

Has Abstract

pub_date

2011-04-01 00:00:00

pages

250-5

issue

4

eissn

1976-6696

issn

1976-670X

journal_volume

44

pub_type

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